用于长期监测的可穿戴步态装置

IF 0.3 Q4 MULTIDISCIPLINARY SCIENCES
ION CACIULA, GIORGIAN MARIUS IONITA, HENRI GEORGE COANDA, DINU COLTUC, NICOLETA ANGELESCU, FELIX ALBU, DANIELA HAGIESCU
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引用次数: 0

摘要

本研究描述了一种使用ESP32微控制器和ICM-20948模块的低成本、易于部署的步态监测系统。ESP32微控制器从ICM-20948模块收集数据,这些数据用于训练卷积神经网络(CNN)将步态模式分为两类:正常和病理。结果表明,该系统对正常步态样本的正确率为97.05%,对病理步态样本的正确率为84.54%,能够达到较高的步态二值分类准确率。采用校准后的双采集数字万用表测量器件的功耗。预计工作时间约为12小时,电池容量为1800毫安时的LiPo型。因此,它可以用于跟踪神经系统疾病患者的步态或评估步态康复治疗的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WEARABLE GAIT DEVICE FOR LONG-TERM MONITORING
This study describes a low-cost and easy to deploy gait monitoring system that uses an ESP32 microcontroller and an ICM-20948 module. The ESP32 microcontroller collects data from the ICM-20948 module and these data are used to train a convolutional neural network (CNN) to classify gait patterns into two categories: normal and pathological. The results show that the system can achieve a high accuracy for binary gait classification, being able to correctly classify 97.05% of the normal gait samples and 84.54% of the pathological gait samples. The power consumption of the devive was measured using a calibrated and dual-acquisition digital multimeter. The estimated operating time was around 12 hours, with a battery capacity of 1800 mAh LiPo type. Therefore, it could be used to track the gait of patients with neurological disorders or to assess the effectiveness of gait rehabilitation treatments.
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来源期刊
Journal of Science and Arts
Journal of Science and Arts MULTIDISCIPLINARY SCIENCES-
自引率
25.00%
发文量
57
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